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IEEE International Conference on Signal and image processing Applications (ICSIPA)

作者： K. Seetharaman Department of Computer Science and Engineering Annamalai University Tamil Nadu India

A statistical approach, based on a family of Full Range Autoregressive models, is proposed for texture analysis. Bayesian approach is adopted to estimate the model parameters. Using the parameters, autocorrelation (AC) coefficient is computed. Based on the AC, two texture descriptors are proposed: (i) texnum, the local descriptor and (ii) texspectrum, the global descriptor. Decimal numbers are computed and that are proposed to represent textures present in a small image region. These numbers uniquely represent the texture primitives. The textured image under analysis is represented globally by observing the frequency of occurrences of the texnums called texspectrum. The textures are identified and are distinguished from the untextured regions with edges.

关键词： stochastic processes image texture analysis Pixel image analysis Signal analysis Bayesian methods Autocorrelation image segmentation Markov random fields Signal processing

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A robust iterative multiframe SRR based on Andrew's Sine stochastic estimation with Andrew;s Sine-Tikhonov regularization

A robust iterative multiframe SRR based on Andrew's Sine sto...

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IEEE International Symposium on Intelligent Signal processing and Communication Systems (ISPACS)

作者： Vorapoj Patanavijit Faculty of Engineering Assumption University Bangkok Thailand

Traditionally, the concept of super resolution reconstruction (SRR) relates to a process whereby images are obtained with resolutions that are beyond the limiting factors of the uncompensated imaging system. Many such SRR algorithms have been proposed during this decade but almost SRR estimations are based on L1 or L2 statistical norm estimation therefore these SRR algorithms are usually very sensitive to their assumed model of data and noise that limits their utility. This paper proposes a novel SRR algorithm based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Andrew's Sine norm is proposed for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Moreover, Tikhonov regularization and Andrew's Sine-Tikhonov regularization are proposed to remove artifacts from the final answer and improve the rate of convergence. A number of experimental results are presented to demonstrate the efficacy of the proposed algorithm in comparison to other super-resolution algorithms based on L1 and L2 norm for a several noise models such as noiseless, AWGN, Poisson, Salt & Pepper Noise and Speckle Noise.

关键词： Robustness stochastic processes Additive white noise Gaussian noise image resolution image reconstruction Iterative algorithms stochastic resonance Bayesian methods Cost function

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An efficient stochastic approach to groupwise non-rigid image registration

An efficient stochastic approach to groupwise non-rigid imag...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Kirill A. Sidorov Stephen Richmond David Marshall School of Computer Science Cardiff University UK School of Dentistry Cardiff University UK

The groupwise approach to non-rigid image registration, solving the dense correspondence problem, has recently been shown to be a useful tool in many applications, including medical imaging, automatic construction of statistical models of appearance and analysis of facial dynamics. Such an approach overcomes limitations of traditional pairwise methods but at a cost of having to search for the solution (optimal registration) in a space of much higher dimensionality which grows rapidly with the number of examples (images) being registered. Techniques to overcome this dimensionality problem have not been addressed sufficiently in the groupwise registration literature. In this paper, we propose a novel, fast and reliable, fully unsupervised stochastic algorithm to search for optimal groupwise dense correspondence in large sets of unmarked images. The efficiency of our approach stems from novel dimensionality reduction techniques specific to the problem of groupwise image registration and from comparative insensitivity of the adopted optimisation scheme (simultaneous perturbation stochastic approximation (SPSA)) to the high dimensionality of the search space. Additionally, our algorithm is formulated in way readily suited to implementation on graphics processing units (GPU). In evaluation of our method we show a high robustness and success rate, fast convergence on various types of test data, including facial images featuring large degrees of both inter- and intra-person variation, and show considerable improvement in terms of accuracy of solution and speed compared to traditional methods.

关键词： stochastic processes image registration Computer science Application software Biomedical imaging image analysis Robustness Dentistry Cost function Graphics

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Effects of additional independent noise in binary composite hypothesis-testing problems

Effects of additional independent noise in binary composite ...

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International Conference on Signal processing and Communication Systems, ICSPCS

作者： Suat Bayram Sinan Gezici Department of Electrical and Electronics Engineering Bilkent University Ankara Turkey

Performance of some suboptimal detectors can be improved by adding independent noise to their observations. In this paper, the effects of adding independent noise to observations of a detector are investigated for binary composite hypothesis-testing problems in a generalized Neyman-Pearson framework. Sufficient conditions are derived to determine when performance of a detector can or cannot be improved via additional independent noise. Also, upper and lower limits are derived on the performance of a detector in the presence of additional noise, and statistical characterization of optimal additional noise is provided. In addition, two optimization techniques are proposed to calculate the optimal additional noise. Finally, simulation results are presented to investigate the theoretical results.

关键词： Detectors Costs Sufficient conditions Bayesian methods Minimax techniques stochastic resonance Testing Digital communication image processing Robots

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Determining image origin and integrity using sensor noise

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2008年第1期3卷 74-90页

作者： Chen, Mo Fridrich, Jessica GoIjan, Miroslav Lukas, Jan JADAK Technol Syracuse NY 13212 USA SUNY Binghamton Binghamton NY 13902 USA Honeywell Global Design Ctr Aerosp Adv Technol Brno 63900 Czech Republic

In this paper, we provide a unified framework for identifying the source digital camera from its images and for revealing digitally altered images using photo-response nonuniformity noise (PRNU), which is a unique stochastic fingerprint of imaging sensors. The PRNU is obtained using a maximum-likelihood estimator derived from a simplified model of the sensor output. Both digital forensics tasks are then achieved by detecting the presence of sensor PRNU in specific regions of the image under investigation. The detection is formulated as a hypothesis testing problem. The statistical distribution of the optimal test statistics is obtained using a predictor of the test statistics on small image blocks. The predictor enables more accurate and meaningful estimation of probabilities of false rejection of a correct camera and missed detection of a tampered region. We also include a benchmark implementation of this framework and detailed experimental validation. The robustness of the proposed forensic methods is tested on common image processing, such as JPEG compression, gamma correction, resizing, and denoising.

关键词： authentication camera identification digital forensic digital forgery imaging sensor integrity verification photo-response nonuniformity

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A non-linear dimension reduction methodology for generating data-driven stochastic input models

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JOURNAL OF COMPUTATIONAL PHYSICS 2008年第13期227卷 6612-6637页

作者： Ganapathysubramanian, Baskar Zabaras, Nicholas Cornell Univ Sibley Sch Mech & Aerosp Engn Mat Proc Design & Control Lab Ithaca NY 14853 USA

stochastic analysis of random heterogeneous media (polycrystalline materials, porous media, functionally graded materials) provides information of significance only if realistic input models of the topology and property variations are used. This paper proposes a framework to construct such input stochastic models for the topology and thermal diffusivity variations in heterogeneous media using a data-driven strategy. Given a set of microstructure realizations (input samples) generated from given statistical information about the medium topology, the framework constructs a reduced-order stochastic representation of the thermal diffusivity. This problem of constructing a low-dimensional stochastic representation of property variations is analogous to the problem of manifold learning and parametric fitting of hyper-surfaces encountered in image processing and psychology. Denote by M the set of microstructures that satisfy the given experimental statistics. A non-linear dimension reduction strategy is utilized to map M to a low-dimensional region, A. We first show that M is a compact manifold embedded in a high-dimensional input space R-n. An isometric mapping F from M to a low-dimensional, compact, connected set ACR(d) (d << n) is constructed. Given only a finite set of samples of the data, the methodology uses arguments from graph theory and differential geometry to construct the isometric transformation F : M -> A. Asymptotic convergence of the representation of M by A is shown. This mapping F serves as an accurate, low-dimensional, data-driven representation of the property variations. The reduced-order model of the material topology and thermal diffusivity variations is subsequently used as an input in the solution of stochastic partial differential equations that describe the evolution of dependant variables. A sparse grid collocation strategy (Smolyak algorithm) is utilized to solve these stochastic equations efficiently. We showcase the methodology by constructin

关键词： stochastic partial differential equations data-driven models non-linear embedding manifold learning model reduction collocation methods sparse grids microstructure

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image guided respiratory motion analysis: Time series and image registration

Image guided respiratory motion analysis: Time series and im...

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作者： Ruan, Dan University of Michigan

学位级别：Ph.D.

The efficacy of image guided radiation therapy (IGRT) systems relies on accurately extracting, modeling and predicting tumor movement with imaging techniques. This thesis investigates two key problems associated with such systems: motion modeling and image processing. For thoracic and upper abdominal tumors, respiratory motion is the dominant factor for tumor movement. We have studied several special structured time series analysis techniques to incorporate the semi-periodicity characteristics of respiratory motion. The proposed methods are robust towards large variations among fractions and populations; the algorithms perform stably in the presence of sparse radiographic observations with noise. We have proposed a subspace projection method to quantitatively evaluate the semi-periodicity of a given observation trace; a nonparametric local regression approach for real-time prediction of respiratory motion; a state augmentation scheme to model hysteresis; and an ellipse tracking algorithm to estimate the trend of respiratory motion in real time. For image processing, we have focused on designing regularizations to account for prior information in image registration problems. We investigated a penalty function design that accommodates tissue-type-dependent elasticity information. We studied a class of discontinuity preserving regularizers that yield smooth deformation estimates in most regions, yet allow discontinuities supported by data. We have further proposed a discriminate regularizer that preserves shear discontinuity, but discourages folding or vacuum generating flows. In addition, we have initiated a preliminary principled study on the fundamental performance limit of image registration problems. We proposed a statistical generative model to account for noise effect in both source and target images, and investigated the approximate performance of the maximum-likelihood estimator corresponding to the generative model and the commonly adopted M-estimator. A simp

关键词： Time Series Analysis image Registration statistical Signal processing Robust Estimation stochastic Approximation Thesis

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Investigation of Nonlinearity in Hyperspectral imagery Using Surrogate Data methods

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2008年第10期46卷 2840-2847页

作者： Han, Tian Goodenough, David G. Minist Agr & Lands Integrated Land Management Bur Victoria BC V8W 9M2 Canada Univ Victoria Victoria BC V8W 2Y2 Canada Nat Resources Canada Pacific Forestry Ctr Victoria BC V8Z 1M5 Canada

Although hyperspectral remotely sensed data are believed to be nonlinear, they are often modeled and processed by algorithms assuming that the data are realizations of some linear stochastic processes. This is likely due to the reason that either the nonlinearity of the data may not be strong enough, and the algorithms based on linear data assumption may still do the job, or the effective algorithms that are capable of dealing with nonlinear data are not widely available. The simplification on data characteristics, however, may compromise the effectiveness and accuracy of information extraction from hyperspectral imagery. In this paper, we are investigating the existence of nonlinearity in hyperspectral data represented by a 4-m Airborne Visible/Infrared Imaging Spectrometer image acquired over an area of coastal forests on Vancouver Island. The method employed for the investigation is based on the statistical test using surrogate data, an approach often used in nonlinear time series analysis. In addition to the high-order autocorrelation, spectral angle is utilized as the discriminating statistic to evaluate the differences between the hyperspectral data and their surrogates. To facilitate the statistical test, simulated data sets are created under linear stochastic constraints. Both simulated and real hyperspectral data are rearranged into a set of spectral series where the spectral and spatial adjacency of the original data is maintained as much as possible. This paper reveals that the differences are statistically significant between the values of discriminating statistics derived from the hyperspectral data and their surrogates. This indicates that the selected hyperspectral data are nonlinear in the spectral domain. Algorithms that are capable of explicitly addressing the nonlinearity are needed for processing hyperspectral remotely sensed data.

关键词： Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral nonlinearity statistical test surrogate

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Investigation of Nonlinearity in Hyperspectral imagery Using Surrogate Data methods

Investigation of Nonlinearity in Hyperspectral Imagery Using...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

关键词： Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral nonlinearity statistical test surrogate

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image processing for Computer Graphics and Vision

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2008年

作者： Luiz Velho Alejandro C. Frery Jonas Gomes

ISBN: (纸本)9781848001923

image processing is concerned with the analysis and manipulation of images by computer. Providing a thorough treatment of image processing with an emphasis on those aspects most used in computer graphics, the authors concentrate on describing and analyzing the underlying concepts rather than on presenting algorithms or pseudocode. As befits a modern introduction to this topic, a good balance is struck between discussing the underlying mathematics and the main topics: signal processing, data discretization, the theory of colour and different colour systems, operations in images, dithering and half-toning, warping and morphing and image processing. This second edition reflects recent trends in science andtechnology that exploit image processing in computer graphics and vision applications. stochastic image models and statistical methods for image processing are covered as are: A modern approach and new developments in the area, Probability theory for image processing, Applications in image analysis and computer vision.

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